CNN for Handwritten Arabic Digits Recognition Based on LeNet-5

El‐Sawy, A. A.; El-Bakry, Hazem M.; Loey, Mohamed

doi:10.1007/978-3-319-48308-5_54

Cited by 177 publications

(96 citation statements)

References 18 publications

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“…Convolutional Neural Networks have accomplished extraordinary success for medical image/video classification and detection. In 2012, Ciregan et al and Krizhevsky and et al [28,29] showed how CNNs based on Graphics Processing Unit (GPU) can enhance many vision benchmark records such as MNIST [30], Chinese characters [31], Arabic digits recognition [32], Arabic handwritten characters recognition [33], NORB (jittered, cluttered) [34], traffic signs [35], and large-scale ImageNet [36] benchmarks. In the following years, various advances in ConvNets further increased the accuracy rate on the image detection/classification competition tasks.…”

Section: Convolution Neural Networkmentioning

confidence: 99%

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

2020

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The coronavirus pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

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Section: Convolution Neural Networkmentioning

confidence: 99%

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

2020

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“…To perform training and classification with a multi-image augmented CNN model the basic architecture of LeNet [9] model is exploited. It is used to predict COVID and non-COVID cases from CT Scan and X-ray images of lungs.…”

Section: Training and Classification Of Cnn Based Deep Learning Modelmentioning

confidence: 99%

COVID-19 Detection on Chest X-Ray and CT Scan Images Using Multi-image Augmented Deep Learning Model

Purohit

Kesarwani

Kisku

et al. 2020

Preprint

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COVID-19 is posed as very infectious and deadly pneumonia type disease until recent time. Novel coronavirus or SARS-COV-2 strain is responsible for COVID-19 and it has already shown the deadly nature of respiratory disease by threatening the health of millions of lives across the globe. Clinical study reveals that a COVID-19 infected person may experience dry cough, muscle pain, headache, fever, sore throat and mild to moderate respiratory illness. At the same time, it affects the lungs badly with virus infection. So, the lung can be a prominent internal organ to diagnose the gravity of COVID-19 infection using X-Ray and CT scan images of chest. Despite having lengthy testing time, RT-PCR is a proven testing methodology to detect coronavirus infection. Sometimes, it might give more false positive and false negative results than the desired rates. Therefore, to assist the traditional RT-PCR methodology for accurate clinical diagnosis, COVID-19 screening can be adopted with X-Ray and CT scan images of lung of an individual. This image based diagnosis will bring radical change in detecting coronavirus infection in human body with ease and having zero or near to zero false positives and false negatives rates. This paper reports a convolutional neural network (CNN) based multi-image augmentation technique for detecting COVID-19 in chest X-Ray and chest CT scan images of coronavirus suspected individuals. Multi-image augmentation makes use of discontinuity information obtained in the filtered images for increasing the number of effective examples for training the CNN model. With this approach, the proposed model exhibits higher classification accuracy around 95.38% and 98.97% for CT scan and X-Ray images respectively. CT scan images with multi-image augmentation achieves sensitivity of 94.78% and specificity of 95.98%, whereas X-Ray images with multi-image augmentation achieves sensitivity of 99.07% and specificity of 98.88%. Evaluation has been done on publicly available databases containing both chest X-Ray and CT scan images and the experimental results are also compared with ResNet-50 and VGG-16 models.

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“…The CNN model was trained with a dropout technique under the Theano framework. Ahmed El-Sawy et al [ 32 ] suggested the Deep convolutional neural networks (DCNN)model for the recognition of isolated handwritten Arabic characters. They proposed a dataset referred to as Arabic handwritten characters dataset AHCD.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognition of Pashto Handwritten Characters Based on Deep Learning

Amin

Yasir

Ahn

2020

Sensors

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Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto’s hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network (CNN) model for recognition of Pashto hand-written characters for the first time in an unrestricted environment. Firstly, a novel Pashto handwritten character data set, “Poha”, for 44 characters is constructed. For preprocessing, deep fusion image processing techniques and noise reduction for text optimization are applied. A CNN model optimized in the number of convolutional layers and their parameters outperformed common deep models in terms of accuracy. Moreover, a set of benchmark popular CNN models applied to Poha is evaluated and compared with the proposed model. The obtained experimental results show that the proposed model is superior to other models with test accuracy of 99.64 percent for PHCR. The results indicate that our model may be a strong candidate for handwritten character recognition and automated PHCR applications.

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CNN for Handwritten Arabic Digits Recognition Based on LeNet-5

Cited by 177 publications

References 18 publications

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

COVID-19 Detection on Chest X-Ray and CT Scan Images Using Multi-image Augmented Deep Learning Model

Recognition of Pashto Handwritten Characters Based on Deep Learning

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